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BACKGROUND: Tandem repeats are specific sequences in genomic DNA repeated in tandem that are present in all organisms. Among the subcategories of TRs we have Satellite repeats, that is divided into macrosatellites, minisatellites, and microsatellites, being the last two of specific interest because they can identify polymorphisms between organisms due to their instability. Currently, most mining tools focus on Simple Sequence Repeats (SSR) mining, and only a few can identify SSRs in the coding regions. RESULTS: We developed a microsatellite mining software called SATIN (Micro and Mini SATellite IdentificatioN tool) based on a new sliding window algorithm written in C and Python. It represents a new approach to SSR mining by addressing the limitations of existing tools, particularly in coding region SSR mining. SATIN is available at https://github.com/labgm/SATIN.git . It was shown to be the second fastest for perfect and compound SSR mining. It can identify SSRs from coding regions plus SSRs with motif sizes bigger than 6. Besides the SSR mining, SATIN can also analyze SSRs polymorphism on coding-regions from pre-determined groups, and identify SSRs differentially abundant among them on a per-gene basis. To validate, we analyzed SSRs from two groups of Escherichia coli (K12 and O157) and compared the results with 5 known SSRs from coding regions. SATIN identified all 5 SSRs from 237 genes with at least one SSR on it. CONCLUSIONS: The SATIN is a novel microsatellite search software that utilizes an innovative sliding window technique based on a numerical list for repeat region search to identify perfect, and composite SSRs while generating comprehensible and analyzable outputs. It is a tool capable of using files in fasta or GenBank format as input for microsatellite mining, also being able to identify SSRs present in coding regions for GenBank files. In conclusion, we expect SATIN to help identify potential SSRs to be used as genetic markers.

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Microsatellites, also known as SSRs or STRs, are polymorphic DNA regions with tandem repetitions of a nucleotide motif of size 1-6 base pairs with a broad range of applications in many fields, such as comparative genomics, molecular biology, and forensics. However, the majority of researchers do not have computational training and struggle while running command-line tools or very limited web tools for their SSR research, spending a considerable amount of time learning how to execute the software and conducting the post-processing data tabulation in other tools or manually-time that could be used directly in data analysis. We present EasySSR, a user-friendly web tool with command-line full functionality, designed for practical use in batch identifying and comparing SSRs in sequences, draft, or complete genomes, not requiring previous bioinformatic skills to run. EasySSR requires only a FASTA and an optional GENBANK file of one or more genomes to identify and compare STRs. The tool can automatically analyze and compare SSRs in whole genomes, convert GenBank to PTT files, identify perfect and imperfect SSRs and coding and non-coding regions, compare their frequencies, abundancy, motifs, flanking sequences, and iterations, producing many outputs ready for download such as PTT files, interactive charts, and Excel tables, giving the user the data ready for further analysis in minutes. EasySSR was implemented as a web application, which can be executed from any browser and is available for free at https://computationalbiology.ufpa.br/easyssr/. Tutorials, usage notes, and download links to the source code can be found at https://github.com/engbiopct/EasySSR.

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OBJECTIVES: The horizontal transfer of antibiotic resistance genes in Gram-negative bacteria, mainly through plasmids, is one of the greatest concerns for health systems worldwide and has been a growing threat in hospitals related to healthcare-associated infections by multidrug-resistant bacteria. Here we present p henotypic and genomic characterization of a KPC-2 and MCR-1.27-producing Klebsiella pneumoniae strain isolated from a paediatric patient at an oncologic hospital in Belém, Pará State, Brazilian Amazon region. METHODS: Antibiotic susceptibility test, whole genome sequencing, and in silico analysis were used to characterize the bacterial isolate (IEC48020) received in the Evandro Chagas Institute. RESULTS: The isolate was resistant to carbapenems, colistin, polymyxin B, and several other antimicrobials and was susceptible in vitro just to tigecycline, classified as an extensively drug-resistant phenotype. Genomic analysis revealed IEC48020 strain belonged to sequence type 11, clonal complex 258 high-risk clone and the presence of eight plasmids, two of them harbouring mcr-1.27 and blaKPC-2 genes, and the presence of virulence-related genes encoding yersiniabactin, phospholipase D, and traT genes. CONCLUSIONS: The presence and dissemination of high-risk clone bacteria with high disseminating plasmids containing antibiotic resistance genes for last resource antibiotics treatment options is a threat to the healthcare system and demands efforts in surveillance and epidemiological research for better knowledge of the actual situation of antibiotic resistance in the healthcare system, especially in the Amazon region, Brazil.

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